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Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory

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  • Song, Mengmeng
  • Zhang, Huixian
  • Xing, Xinyu
  • Duan, Yucong

Abstract

Failures in human-chatbot interactions are becoming inevitable such as failure of chatbots to maintain contextual awareness. Thus, an effective service recovery strategy is essential for e-commerce enterprises to restore customers and resolve their complaints. Therefore, based on politeness theory, this study explores the fundamental mechanism and boundary conditions for a chatbot politeness strategy (appreciation vs. apology) on consumers' post-recovery satisfaction using four scenario-based experiments. The results indicate that establishing a good human-chatbot relationship (appreciation) is a more effective recovery strategy than admitting the chatbot's limited competence (apology) in redressing service failures; face concern mediates the effect of the politeness strategy on post-recovery satisfaction; and time pressure plays a moderating role in the effect of the politeness strategy on face concern and post-recovery satisfaction. This study extends the research of politeness theory and face concern in the field of chatbot marketing, and provide practical guidance for e-commerce enterprises to deal effectively with chatbot failure.

Suggested Citation

  • Song, Mengmeng & Zhang, Huixian & Xing, Xinyu & Duan, Yucong, 2023. "Appreciation vs. apology: Research on the influence mechanism of chatbot service recovery based on politeness theory," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:joreco:v:73:y:2023:i:c:s096969892300070x
    DOI: 10.1016/j.jretconser.2023.103323
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    References listed on IDEAS

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    Cited by:

    1. Zhou, Cheng & Chang, Qian, 2024. "Informational or emotional? Exploring the relative effects of chatbots’ self-recovery strategies on consumer satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    2. Kim, Hayeon & Lee, Sang Woo & Seo, Sungwoo, 2024. "Strategies for Addressing Hallucinations in Generative AI: Exploring the Roles of Politeness, Attribution, and Anthropomorphism," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302511, International Telecommunications Society (ITS).
    3. Li, Tian-Ge & Zhang, Chu-Bing & Chang, Ying & Zheng, Wei, 2024. "The impact of AI identity disclosure on consumer unethical behavior: A social judgment perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    4. Dong, Lingfeng & Wu, Zhenwei & Ji, Ting & Tu, Yu, 2024. "Informing or persuading? Unveiling the complex dynamics of live chat in online labor markets," Journal of Retailing and Consumer Services, Elsevier, vol. 80(C).
    5. Wang, Ping & Li, Kunyang & Du, Qinglong & Wang, Jianqiong, 2024. "Customer experience in AI-enabled products: Scale development and validation," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    6. Guan, Dongxiao & Lei, Yunfei & Liu, Yu & Ma, Qinhai, 2024. "The effect of matching promotion type with purchase type on green consumption," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).
    7. Zhang, Huixian & Song, Mengmeng, 2024. "Optimizing service encounters through mascot-like robot with a politeness strategy," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    8. Pelau Corina & Pop Stefana & Ciofu Ioana, 2024. "Scenario-Based Approach to AI’s Agency to Perform Human-Specific Tasks," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 2311-2318.

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